// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.

#include "main.h"

#include <Eigen/CXX11/Tensor>

using Eigen::Tensor;

template<typename>
static void
test_simple_reshape()
{
	Tensor<float, 5> tensor1(2, 3, 1, 7, 1);
	tensor1.setRandom();

	Tensor<float, 3> tensor2(2, 3, 7);
	Tensor<float, 2> tensor3(6, 7);
	Tensor<float, 2> tensor4(2, 21);

	Tensor<float, 3>::Dimensions dim1(2, 3, 7);
	tensor2 = tensor1.reshape(dim1);
	Tensor<float, 2>::Dimensions dim2(6, 7);
	tensor3 = tensor1.reshape(dim2);
	Tensor<float, 2>::Dimensions dim3(2, 21);
	tensor4 = tensor1.reshape(dim1).reshape(dim3);

	for (int i = 0; i < 2; ++i) {
		for (int j = 0; j < 3; ++j) {
			for (int k = 0; k < 7; ++k) {
				VERIFY_IS_EQUAL(tensor1(i, j, 0, k, 0), tensor2(i, j, k));
				VERIFY_IS_EQUAL(tensor1(i, j, 0, k, 0), tensor3(i + 2 * j, k));
				VERIFY_IS_EQUAL(tensor1(i, j, 0, k, 0), tensor4(i, j + 3 * k));
			}
		}
	}
}

template<typename>
static void
test_static_reshape()
{
#if defined(EIGEN_HAS_INDEX_LIST)
	using Eigen::type2index;

	Tensor<float, 5> tensor(2, 3, 1, 7, 1);
	tensor.setRandom();

	// New dimensions: [2, 3, 7]
	Eigen::IndexList<type2index<2>, type2index<3>, type2index<7>> dim;
	Tensor<float, 3> reshaped = tensor.reshape(static_cast<Eigen::DSizes<ptrdiff_t, 3>>(dim));

	for (int i = 0; i < 2; ++i) {
		for (int j = 0; j < 3; ++j) {
			for (int k = 0; k < 7; ++k) {
				VERIFY_IS_EQUAL(tensor(i, j, 0, k, 0), reshaped(i, j, k));
			}
		}
	}
#endif
}

template<typename>
static void
test_reshape_in_expr()
{
	MatrixXf m1(2, 3 * 5 * 7 * 11);
	MatrixXf m2(3 * 5 * 7 * 11, 13);
	m1.setRandom();
	m2.setRandom();
	MatrixXf m3 = m1 * m2;

	TensorMap<Tensor<float, 5>> tensor1(m1.data(), 2, 3, 5, 7, 11);
	TensorMap<Tensor<float, 5>> tensor2(m2.data(), 3, 5, 7, 11, 13);
	Tensor<float, 2>::Dimensions newDims1(2, 3 * 5 * 7 * 11);
	Tensor<float, 2>::Dimensions newDims2(3 * 5 * 7 * 11, 13);
	typedef Tensor<float, 1>::DimensionPair DimPair;
	array<DimPair, 1> contract_along{ { DimPair(1, 0) } };
	Tensor<float, 2> tensor3(2, 13);
	tensor3 = tensor1.reshape(newDims1).contract(tensor2.reshape(newDims2), contract_along);

	Map<MatrixXf> res(tensor3.data(), 2, 13);
	for (int i = 0; i < 2; ++i) {
		for (int j = 0; j < 13; ++j) {
			VERIFY_IS_APPROX(res(i, j), m3(i, j));
		}
	}
}

template<typename>
static void
test_reshape_as_lvalue()
{
	Tensor<float, 3> tensor(2, 3, 7);
	tensor.setRandom();

	Tensor<float, 2> tensor2d(6, 7);
	Tensor<float, 3>::Dimensions dim(2, 3, 7);
	tensor2d.reshape(dim) = tensor;

	float scratch[2 * 3 * 1 * 7 * 1];
	TensorMap<Tensor<float, 5>> tensor5d(scratch, 2, 3, 1, 7, 1);
	tensor5d.reshape(dim).device(Eigen::DefaultDevice()) = tensor;

	for (int i = 0; i < 2; ++i) {
		for (int j = 0; j < 3; ++j) {
			for (int k = 0; k < 7; ++k) {
				VERIFY_IS_EQUAL(tensor2d(i + 2 * j, k), tensor(i, j, k));
				VERIFY_IS_EQUAL(tensor5d(i, j, 0, k, 0), tensor(i, j, k));
			}
		}
	}
}

template<typename T, int DataLayout>
static void
test_simple_slice()
{
	Tensor<T, 5, DataLayout> tensor(2, 3, 5, 7, 11);
	tensor.setRandom();

	Tensor<T, 5, DataLayout> slice1(1, 1, 1, 1, 1);
	Eigen::DSizes<ptrdiff_t, 5> indices(1, 2, 3, 4, 5);
	Eigen::DSizes<ptrdiff_t, 5> sizes(1, 1, 1, 1, 1);
	slice1 = tensor.slice(indices, sizes);
	VERIFY_IS_EQUAL(slice1(0, 0, 0, 0, 0), tensor(1, 2, 3, 4, 5));

	Tensor<T, 5, DataLayout> slice2(1, 1, 2, 2, 3);
	Eigen::DSizes<ptrdiff_t, 5> indices2(1, 1, 3, 4, 5);
	Eigen::DSizes<ptrdiff_t, 5> sizes2(1, 1, 2, 2, 3);
	slice2 = tensor.slice(indices2, sizes2);
	for (int i = 0; i < 2; ++i) {
		for (int j = 0; j < 2; ++j) {
			for (int k = 0; k < 3; ++k) {
				VERIFY_IS_EQUAL(slice2(0, 0, i, j, k), tensor(1, 1, 3 + i, 4 + j, 5 + k));
			}
		}
	}
}

template<typename T>
static void
test_const_slice()
{
	const T b[1] = { 42 };
	TensorMap<Tensor<const T, 1>> m(b, 1);
	DSizes<DenseIndex, 1> offsets;
	offsets[0] = 0;
	TensorRef<Tensor<const T, 1>> slice_ref(m.slice(offsets, m.dimensions()));
	VERIFY_IS_EQUAL(slice_ref(0), 42);
}

template<typename T, int DataLayout>
static void
test_slice_in_expr()
{
	typedef Matrix<T, Dynamic, Dynamic, DataLayout> Mtx;
	Mtx m1(7, 7);
	Mtx m2(3, 3);
	m1.setRandom();
	m2.setRandom();

	Mtx m3 = m1.block(1, 2, 3, 3) * m2.block(0, 2, 3, 1);

	TensorMap<Tensor<T, 2, DataLayout>> tensor1(m1.data(), 7, 7);
	TensorMap<Tensor<T, 2, DataLayout>> tensor2(m2.data(), 3, 3);
	Tensor<T, 2, DataLayout> tensor3(3, 1);
	typedef typename Tensor<T, 1>::DimensionPair DimPair;
	array<DimPair, 1> contract_along{ { DimPair(1, 0) } };

	Eigen::DSizes<ptrdiff_t, 2> indices1(1, 2);
	Eigen::DSizes<ptrdiff_t, 2> sizes1(3, 3);
	Eigen::DSizes<ptrdiff_t, 2> indices2(0, 2);
	Eigen::DSizes<ptrdiff_t, 2> sizes2(3, 1);
	tensor3 = tensor1.slice(indices1, sizes1).contract(tensor2.slice(indices2, sizes2), contract_along);

	Map<Mtx> res(tensor3.data(), 3, 1);
	for (int i = 0; i < 3; ++i) {
		for (int j = 0; j < 1; ++j) {
			VERIFY_IS_APPROX(res(i, j), m3(i, j));
		}
	}

	// Take an arbitrary slice of an arbitrarily sized tensor.
	TensorMap<Tensor<const T, 2, DataLayout>> tensor4(m1.data(), 7, 7);
	Tensor<T, 1, DataLayout> tensor6 =
		tensor4.reshape(DSizes<ptrdiff_t, 1>(7 * 7)).exp().slice(DSizes<ptrdiff_t, 1>(0), DSizes<ptrdiff_t, 1>(35));
	for (int i = 0; i < 35; ++i) {
		VERIFY_IS_APPROX(tensor6(i), expf(tensor4.data()[i]));
	}
}

template<typename T, int DataLayout>
static void
test_slice_as_lvalue()
{
	Tensor<T, 3, DataLayout> tensor1(2, 2, 7);
	tensor1.setRandom();
	Tensor<T, 3, DataLayout> tensor2(2, 2, 7);
	tensor2.setRandom();
	Tensor<T, 3, DataLayout> tensor3(4, 3, 5);
	tensor3.setRandom();
	Tensor<T, 3, DataLayout> tensor4(4, 3, 2);
	tensor4.setRandom();
	Tensor<T, 3, DataLayout> tensor5(10, 13, 12);
	tensor5.setRandom();

	Tensor<T, 3, DataLayout> result(4, 5, 7);
	Eigen::DSizes<ptrdiff_t, 3> sizes12(2, 2, 7);
	Eigen::DSizes<ptrdiff_t, 3> first_slice(0, 0, 0);
	result.slice(first_slice, sizes12) = tensor1;
	Eigen::DSizes<ptrdiff_t, 3> second_slice(2, 0, 0);
	result.slice(second_slice, sizes12).device(Eigen::DefaultDevice()) = tensor2;

	Eigen::DSizes<ptrdiff_t, 3> sizes3(4, 3, 5);
	Eigen::DSizes<ptrdiff_t, 3> third_slice(0, 2, 0);
	result.slice(third_slice, sizes3) = tensor3;

	Eigen::DSizes<ptrdiff_t, 3> sizes4(4, 3, 2);
	Eigen::DSizes<ptrdiff_t, 3> fourth_slice(0, 2, 5);
	result.slice(fourth_slice, sizes4) = tensor4;

	for (int j = 0; j < 2; ++j) {
		for (int k = 0; k < 7; ++k) {
			for (int i = 0; i < 2; ++i) {
				VERIFY_IS_EQUAL(result(i, j, k), tensor1(i, j, k));
				VERIFY_IS_EQUAL(result(i + 2, j, k), tensor2(i, j, k));
			}
		}
	}
	for (int i = 0; i < 4; ++i) {
		for (int j = 2; j < 5; ++j) {
			for (int k = 0; k < 5; ++k) {
				VERIFY_IS_EQUAL(result(i, j, k), tensor3(i, j - 2, k));
			}
			for (int k = 5; k < 7; ++k) {
				VERIFY_IS_EQUAL(result(i, j, k), tensor4(i, j - 2, k - 5));
			}
		}
	}

	Eigen::DSizes<ptrdiff_t, 3> sizes5(4, 5, 7);
	Eigen::DSizes<ptrdiff_t, 3> fifth_slice(0, 0, 0);
	result.slice(fifth_slice, sizes5) = tensor5.slice(fifth_slice, sizes5);
	for (int i = 0; i < 4; ++i) {
		for (int j = 2; j < 5; ++j) {
			for (int k = 0; k < 7; ++k) {
				VERIFY_IS_EQUAL(result(i, j, k), tensor5(i, j, k));
			}
		}
	}
}

template<typename T, int DataLayout>
static void
test_slice_raw_data()
{
	Tensor<T, 4, DataLayout> tensor(3, 5, 7, 11);
	tensor.setRandom();

	Eigen::DSizes<ptrdiff_t, 4> offsets(1, 2, 3, 4);
	Eigen::DSizes<ptrdiff_t, 4> extents(1, 1, 1, 1);
	typedef TensorEvaluator<decltype(tensor.slice(offsets, extents)), DefaultDevice> SliceEvaluator;
	auto slice1 = SliceEvaluator(tensor.slice(offsets, extents), DefaultDevice());
	VERIFY_IS_EQUAL(slice1.dimensions().TotalSize(), 1);
	VERIFY_IS_EQUAL(slice1.data()[0], tensor(1, 2, 3, 4));

	if (DataLayout == ColMajor) {
		extents = Eigen::DSizes<ptrdiff_t, 4>(2, 1, 1, 1);
		auto slice2 = SliceEvaluator(tensor.slice(offsets, extents), DefaultDevice());
		VERIFY_IS_EQUAL(slice2.dimensions().TotalSize(), 2);
		VERIFY_IS_EQUAL(slice2.data()[0], tensor(1, 2, 3, 4));
		VERIFY_IS_EQUAL(slice2.data()[1], tensor(2, 2, 3, 4));
	} else {
		extents = Eigen::DSizes<ptrdiff_t, 4>(1, 1, 1, 2);
		auto slice2 = SliceEvaluator(tensor.slice(offsets, extents), DefaultDevice());
		VERIFY_IS_EQUAL(slice2.dimensions().TotalSize(), 2);
		VERIFY_IS_EQUAL(slice2.data()[0], tensor(1, 2, 3, 4));
		VERIFY_IS_EQUAL(slice2.data()[1], tensor(1, 2, 3, 5));
	}

	extents = Eigen::DSizes<ptrdiff_t, 4>(1, 2, 1, 1);
	auto slice3 = SliceEvaluator(tensor.slice(offsets, extents), DefaultDevice());
	VERIFY_IS_EQUAL(slice3.dimensions().TotalSize(), 2);
	VERIFY_IS_EQUAL(slice3.data(), static_cast<T*>(0));

	if (DataLayout == ColMajor) {
		offsets = Eigen::DSizes<ptrdiff_t, 4>(0, 2, 3, 4);
		extents = Eigen::DSizes<ptrdiff_t, 4>(3, 2, 1, 1);
		auto slice4 = SliceEvaluator(tensor.slice(offsets, extents), DefaultDevice());
		VERIFY_IS_EQUAL(slice4.dimensions().TotalSize(), 6);
		for (int i = 0; i < 3; ++i) {
			for (int j = 0; j < 2; ++j) {
				VERIFY_IS_EQUAL(slice4.data()[i + 3 * j], tensor(i, 2 + j, 3, 4));
			}
		}
	} else {
		offsets = Eigen::DSizes<ptrdiff_t, 4>(1, 2, 3, 0);
		extents = Eigen::DSizes<ptrdiff_t, 4>(1, 1, 2, 11);
		auto slice4 = SliceEvaluator(tensor.slice(offsets, extents), DefaultDevice());
		VERIFY_IS_EQUAL(slice4.dimensions().TotalSize(), 22);
		for (int l = 0; l < 11; ++l) {
			for (int k = 0; k < 2; ++k) {
				VERIFY_IS_EQUAL(slice4.data()[l + 11 * k], tensor(1, 2, 3 + k, l));
			}
		}
	}

	if (DataLayout == ColMajor) {
		offsets = Eigen::DSizes<ptrdiff_t, 4>(0, 0, 0, 4);
		extents = Eigen::DSizes<ptrdiff_t, 4>(3, 5, 7, 2);
		auto slice5 = SliceEvaluator(tensor.slice(offsets, extents), DefaultDevice());
		VERIFY_IS_EQUAL(slice5.dimensions().TotalSize(), 210);
		for (int i = 0; i < 3; ++i) {
			for (int j = 0; j < 5; ++j) {
				for (int k = 0; k < 7; ++k) {
					for (int l = 0; l < 2; ++l) {
						int slice_index = i + 3 * (j + 5 * (k + 7 * l));
						VERIFY_IS_EQUAL(slice5.data()[slice_index], tensor(i, j, k, l + 4));
					}
				}
			}
		}
	} else {
		offsets = Eigen::DSizes<ptrdiff_t, 4>(1, 0, 0, 0);
		extents = Eigen::DSizes<ptrdiff_t, 4>(2, 5, 7, 11);
		auto slice5 = SliceEvaluator(tensor.slice(offsets, extents), DefaultDevice());
		VERIFY_IS_EQUAL(slice5.dimensions().TotalSize(), 770);
		for (int l = 0; l < 11; ++l) {
			for (int k = 0; k < 7; ++k) {
				for (int j = 0; j < 5; ++j) {
					for (int i = 0; i < 2; ++i) {
						int slice_index = l + 11 * (k + 7 * (j + 5 * i));
						VERIFY_IS_EQUAL(slice5.data()[slice_index], tensor(i + 1, j, k, l));
					}
				}
			}
		}
	}

	offsets = Eigen::DSizes<ptrdiff_t, 4>(0, 0, 0, 0);
	extents = Eigen::DSizes<ptrdiff_t, 4>(3, 5, 7, 11);
	auto slice6 = SliceEvaluator(tensor.slice(offsets, extents), DefaultDevice());
	VERIFY_IS_EQUAL(slice6.dimensions().TotalSize(), 3 * 5 * 7 * 11);
	VERIFY_IS_EQUAL(slice6.data(), tensor.data());
}

template<typename T, int DataLayout>
static void
test_strided_slice()
{
	typedef Tensor<T, 5, DataLayout> Tensor5f;
	typedef Eigen::DSizes<Eigen::DenseIndex, 5> Index5;
	typedef Tensor<T, 2, DataLayout> Tensor2f;
	typedef Eigen::DSizes<Eigen::DenseIndex, 2> Index2;
	Tensor<T, 5, DataLayout> tensor(2, 3, 5, 7, 11);
	Tensor<T, 2, DataLayout> tensor2(7, 11);
	tensor.setRandom();
	tensor2.setRandom();

	if (true) {
		Tensor2f slice(2, 3);
		Index2 strides(-2, -1);
		Index2 indicesStart(5, 7);
		Index2 indicesStop(0, 4);
		slice = tensor2.stridedSlice(indicesStart, indicesStop, strides);
		for (int j = 0; j < 2; ++j) {
			for (int k = 0; k < 3; ++k) {
				VERIFY_IS_EQUAL(slice(j, k), tensor2(5 - 2 * j, 7 - k));
			}
		}
	}

	if (true) {
		Tensor2f slice(0, 1);
		Index2 strides(1, 1);
		Index2 indicesStart(5, 4);
		Index2 indicesStop(5, 5);
		slice = tensor2.stridedSlice(indicesStart, indicesStop, strides);
	}

	if (true) { // test clamped degenerate interavls
		Tensor2f slice(7, 11);
		Index2 strides(1, -1);
		Index2 indicesStart(-3, 20); // should become 0,10
		Index2 indicesStop(20, -11); // should become 11, -1
		slice = tensor2.stridedSlice(indicesStart, indicesStop, strides);
		for (int j = 0; j < 7; ++j) {
			for (int k = 0; k < 11; ++k) {
				VERIFY_IS_EQUAL(slice(j, k), tensor2(j, 10 - k));
			}
		}
	}

	if (true) {
		Tensor5f slice1(1, 1, 1, 1, 1);
		Eigen::DSizes<Eigen::DenseIndex, 5> indicesStart(1, 2, 3, 4, 5);
		Eigen::DSizes<Eigen::DenseIndex, 5> indicesStop(2, 3, 4, 5, 6);
		Eigen::DSizes<Eigen::DenseIndex, 5> strides(1, 1, 1, 1, 1);
		slice1 = tensor.stridedSlice(indicesStart, indicesStop, strides);
		VERIFY_IS_EQUAL(slice1(0, 0, 0, 0, 0), tensor(1, 2, 3, 4, 5));
	}

	if (true) {
		Tensor5f slice(1, 1, 2, 2, 3);
		Index5 start(1, 1, 3, 4, 5);
		Index5 stop(2, 2, 5, 6, 8);
		Index5 strides(1, 1, 1, 1, 1);
		slice = tensor.stridedSlice(start, stop, strides);
		for (int i = 0; i < 2; ++i) {
			for (int j = 0; j < 2; ++j) {
				for (int k = 0; k < 3; ++k) {
					VERIFY_IS_EQUAL(slice(0, 0, i, j, k), tensor(1, 1, 3 + i, 4 + j, 5 + k));
				}
			}
		}
	}

	if (true) {
		Tensor5f slice(1, 1, 2, 2, 3);
		Index5 strides3(1, 1, -2, 1, -1);
		Index5 indices3Start(1, 1, 4, 4, 7);
		Index5 indices3Stop(2, 2, 0, 6, 4);
		slice = tensor.stridedSlice(indices3Start, indices3Stop, strides3);
		for (int i = 0; i < 2; ++i) {
			for (int j = 0; j < 2; ++j) {
				for (int k = 0; k < 3; ++k) {
					VERIFY_IS_EQUAL(slice(0, 0, i, j, k), tensor(1, 1, 4 - 2 * i, 4 + j, 7 - k));
				}
			}
		}
	}

	if (false) { // tests degenerate interval
		Tensor5f slice(1, 1, 2, 2, 3);
		Index5 strides3(1, 1, 2, 1, 1);
		Index5 indices3Start(1, 1, 4, 4, 7);
		Index5 indices3Stop(2, 2, 0, 6, 4);
		slice = tensor.stridedSlice(indices3Start, indices3Stop, strides3);
	}
}

template<typename T, int DataLayout>
static void
test_strided_slice_write()
{
	typedef Tensor<T, 2, DataLayout> Tensor2f;
	typedef Eigen::DSizes<Eigen::DenseIndex, 2> Index2;

	Tensor<T, 2, DataLayout> tensor(7, 11), tensor2(7, 11);
	tensor.setRandom();
	tensor2 = tensor;
	Tensor2f slice(2, 3);

	slice.setRandom();

	Index2 strides(1, 1);
	Index2 indicesStart(3, 4);
	Index2 indicesStop(5, 7);
	Index2 lengths(2, 3);

	tensor.slice(indicesStart, lengths) = slice;
	tensor2.stridedSlice(indicesStart, indicesStop, strides) = slice;

	for (int i = 0; i < 7; i++)
		for (int j = 0; j < 11; j++) {
			VERIFY_IS_EQUAL(tensor(i, j), tensor2(i, j));
		}
}

template<typename T, int DataLayout>
static void
test_composition()
{
	Eigen::Tensor<T, 2, DataLayout> matrix(7, 11);
	matrix.setRandom();

	const DSizes<ptrdiff_t, 3> newDims(1, 1, 11);
	Eigen::Tensor<T, 3, DataLayout> tensor =
		matrix.slice(DSizes<ptrdiff_t, 2>(2, 0), DSizes<ptrdiff_t, 2>(1, 11)).reshape(newDims);

	VERIFY_IS_EQUAL(tensor.dimensions().TotalSize(), 11);
	VERIFY_IS_EQUAL(tensor.dimension(0), 1);
	VERIFY_IS_EQUAL(tensor.dimension(1), 1);
	VERIFY_IS_EQUAL(tensor.dimension(2), 11);
	for (int i = 0; i < 11; ++i) {
		VERIFY_IS_EQUAL(tensor(0, 0, i), matrix(2, i));
	}
}

template<typename T, int DataLayout>
static void
test_empty_slice()
{
	Tensor<T, 3, DataLayout> tensor(2, 3, 5);
	tensor.setRandom();
	Tensor<T, 3, DataLayout> copy = tensor;

	// empty size in first dimension
	Eigen::DSizes<ptrdiff_t, 3> indices1(1, 2, 3);
	Eigen::DSizes<ptrdiff_t, 3> sizes1(0, 1, 2);
	Tensor<T, 3, DataLayout> slice1(0, 1, 2);
	slice1.setRandom();
	tensor.slice(indices1, sizes1) = slice1;

	// empty size in second dimension
	Eigen::DSizes<ptrdiff_t, 3> indices2(1, 2, 3);
	Eigen::DSizes<ptrdiff_t, 3> sizes2(1, 0, 2);
	Tensor<T, 3, DataLayout> slice2(1, 0, 2);
	slice2.setRandom();
	tensor.slice(indices2, sizes2) = slice2;

	// empty size in third dimension
	Eigen::DSizes<ptrdiff_t, 3> indices3(1, 2, 3);
	Eigen::DSizes<ptrdiff_t, 3> sizes3(1, 1, 0);
	Tensor<T, 3, DataLayout> slice3(1, 1, 0);
	slice3.setRandom();
	tensor.slice(indices3, sizes3) = slice3;

	// empty size in first and second dimension
	Eigen::DSizes<ptrdiff_t, 3> indices4(1, 2, 3);
	Eigen::DSizes<ptrdiff_t, 3> sizes4(0, 0, 2);
	Tensor<T, 3, DataLayout> slice4(0, 0, 2);
	slice4.setRandom();
	tensor.slice(indices4, sizes4) = slice4;

	// empty size in second and third dimension
	Eigen::DSizes<ptrdiff_t, 3> indices5(1, 2, 3);
	Eigen::DSizes<ptrdiff_t, 3> sizes5(1, 0, 0);
	Tensor<T, 3, DataLayout> slice5(1, 0, 0);
	slice5.setRandom();
	tensor.slice(indices5, sizes5) = slice5;

	// empty size in all dimensions
	Eigen::DSizes<ptrdiff_t, 3> indices6(1, 2, 3);
	Eigen::DSizes<ptrdiff_t, 3> sizes6(0, 0, 0);
	Tensor<T, 3, DataLayout> slice6(0, 0, 0);
	slice6.setRandom();
	tensor.slice(indices6, sizes6) = slice6;

	// none of these operations should change the tensor's components
	// because all of the rvalue slices have at least one zero dimension
	for (int i = 0; i < 2; ++i) {
		for (int j = 0; j < 3; ++j) {
			for (int k = 0; k < 5; ++k) {
				VERIFY_IS_EQUAL(tensor(i, j, k), copy(i, j, k));
			}
		}
	}
}

#define CALL_SUBTEST_PART(PART) CALL_SUBTEST_##PART

#define CALL_SUBTESTS_TYPES_LAYOUTS(PART, NAME)                                                                        \
	CALL_SUBTEST_PART(PART)((NAME<float, ColMajor>()));                                                                \
	CALL_SUBTEST_PART(PART)((NAME<float, RowMajor>()));                                                                \
	CALL_SUBTEST_PART(PART)((NAME<bool, ColMajor>()));                                                                 \
	CALL_SUBTEST_PART(PART)((NAME<bool, RowMajor>()))

EIGEN_DECLARE_TEST(cxx11_tensor_morphing)
{
	CALL_SUBTEST_1(test_simple_reshape<void>());
	CALL_SUBTEST_1(test_static_reshape<void>());
	CALL_SUBTEST_1(test_reshape_as_lvalue<void>());
	CALL_SUBTEST_1(test_reshape_in_expr<void>());
	CALL_SUBTEST_1(test_const_slice<float>());

	CALL_SUBTESTS_TYPES_LAYOUTS(2, test_simple_slice);
	CALL_SUBTESTS_TYPES_LAYOUTS(3, test_slice_as_lvalue);
	CALL_SUBTESTS_TYPES_LAYOUTS(4, test_slice_raw_data);
	CALL_SUBTESTS_TYPES_LAYOUTS(5, test_strided_slice_write);
	CALL_SUBTESTS_TYPES_LAYOUTS(6, test_strided_slice);
	CALL_SUBTESTS_TYPES_LAYOUTS(7, test_composition);
}
